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(particularly computer vision), (2) machine learning for data analysis, (3) sensor technologies (e.g., electromagnetic sensors), (4) design and integration of mechanical/electrical devices; and an interest in
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Postdoc: Machine learning for wind flow prediction in coastal dunes Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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prototypes of a quantum computer and a quantum internet by integrating world-class research and groundbreaking innovation. Through excellence, relevance, and leadership, we cultivate a vibrant quantum
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become oriented during film growth will be determined, and also how the films respond to external perturbations, such as molecular guests, light or charge. The focus will be on organic cages thin films as
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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at the atomic scale, using density functional theory-accurate machine-learned potentials and molecular dynamics simulations, in close collaboration with leading European research institutes and steel industry
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In polymeric materials, a fundamental transition is required from synthesis out of fossil-based and one time use to continuous reuse of polymeric products. Current recycling methods cannot recover
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postdoctoral researcher, you will lead the human-computer interaction side of the project. You will investigate how everyday athletes and coaches currently use tracking and feedback technologies, design and
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Trustworthy Graph Machine Learning for Population Scale Networks Job description We invite applications for a postdoctoral researcher to work on fundamental techniques for trustworthy graph machine